Zero Shot Super Resolution

Zero-shot super-resolution (ZSSR) aims to enhance the resolution of low-resolution images without requiring paired high-resolution training data. Current research focuses on leveraging diffusion models, neural radiance fields (NeRFs), and patch-based regularizers to achieve this, often incorporating text guidance or internal reference datasets for improved accuracy and generalization. These advancements are significant because they address the limitations of traditional super-resolution methods that rely on extensive paired datasets, opening possibilities for applications in diverse fields like medical imaging and astronomy where such data is scarce or difficult to obtain.